Hands-On Data Analysis with Pandas

Sensitivity and specificity

Along the lines of the precision and recall trade-off, we have another pair of metrics that can be used to illustrate the delicate balance we strive to achieve with classification problems: sensitivity and specificity.

Sensitivity is the true positive rate, or recall, which we saw previously. Specificity, however, is the true negative rate, or the proportion of true negatives to everything that should have been classified as negative:

Note that, together, specificity and sensitivity consider the full confusion matrix:

We would like to maximize both sensitivity and specificity; however, we could easily maximize ...